Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset

Abstract As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

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Applicability of satellite rainfall estimates for erosion studies in small offshore areas: A case study in Cape Verde Islands

CATENA ◽

10.1016/j.catena.2014.05.029 ◽

2014 ◽

Vol 121 ◽

pp. 365-374 ◽

Cited By ~ 8

Author(s):

Juan Francisco Sanchez-Moreno ◽

Chris M. Mannaerts ◽

Victor Jetten

Keyword(s):

Cape Verde ◽

Cape Verde Islands ◽

Satellite Rainfall ◽

Rainfall Estimates

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Assessment of Satellite Rainfall Estimates as a Pre-Analysis for Water Environment Analytical Tools: A Case Study for Tonle Sap Lake in Cambodia

Engineering Journal ◽

10.4186/ej.2018.22.1.229 ◽

2018 ◽

Vol 22 (1) ◽

pp. 229-241 ◽

Cited By ~ 2

Author(s):

Chanarun Phoeurn ◽

Sarann Ly

Keyword(s):

Water Environment ◽

Tonle Sap Lake ◽

Tonle Sap ◽

Satellite Rainfall ◽

Analytical Tools ◽

Rainfall Estimates

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Validation and Intercomparison of Satellite Rainfall Estimates over Colombia

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2260.1 ◽

2010 ◽

Vol 49 (5) ◽

pp. 1004-1014 ◽

Cited By ~ 109

Author(s):

Tufa Dinku ◽

Franklyn Ruiz ◽

Stephen J. Connor ◽

Pietro Ceccato

Keyword(s):

Complex Terrain ◽

Pacific Coast ◽

Daily Rainfall ◽

Tropical Rainfall Measuring Mission ◽

Poor Performance ◽

Naval Research ◽

Mountain Ranges ◽

The North ◽

Satellite Rainfall ◽

Rainfall Estimates

Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between the two extremes. The evaluated satellite rainfall products are the Tropical Rainfall Measuring Mission 3B42 and 3B42RT products, the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN), the Naval Research Laboratory’s blended product (NRLB), and two versions of the Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMaP_MVK and GSMaP_MVK+). The validation and intercomparison of these products is done for the whole as well as different parts of the country. Validation results are reasonably good for daily rainfall over such complex terrain. The best results were obtained for the eastern plain, and the performance of the products was relatively poor over the Pacific coast. In comparing the different satellite products, it was seen that PERSIANN and GSMaP-MVK exhibited poor performance, with significant overestimation by PERSSIAN and serious underestimation by GSMaP-MVK. CMORPH and GSMaP-MVK+ exhibited the best performance among the products evaluated here.

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Error Propagation of Satellite-Rainfall in Flood Prediction Applications over Complex Terrain: A Case Study in Northeastern Italy

Satellite Rainfall Applications for Surface Hydrology ◽

10.1007/978-90-481-2915-7_13 ◽

2009 ◽

pp. 215-227 ◽

Cited By ~ 1

Author(s):

Efthymios I. Nikolopoulos ◽

Emmanouil N. Anagnostou ◽

Faisal Hossain

Keyword(s):

Complex Terrain ◽

Error Propagation ◽

Flood Prediction ◽

Northeastern Italy ◽

Satellite Rainfall

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A susceptibility-based rainfall threshold approach for landslide occurrence

Natural Hazards and Earth System Science ◽

10.5194/nhess-19-775-2019 ◽

2019 ◽

Vol 19 (4) ◽

pp. 775-789 ◽

Cited By ~ 12

Author(s):

Elise Monsieurs ◽

Olivier Dewitte ◽

Alain Demoulin

Keyword(s):

Landslide Susceptibility ◽

Rainfall Threshold ◽

Exceedance Probability ◽

Equal Weight ◽

Antecedent Rainfall ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Continental Scale ◽

Satellite Rainfall ◽

Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

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